When AI Bends Metal: AI-Assisted Optimization of Design Parameters in Sheet Metal Forming
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911554919727104 |
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| author | Tarraf, Ahmad Kassem-Manthey, Koutaiba Mohammadi, Seyed Ali Martin, Philipp Moj, Lukas Burak, Semih Park, Enju Terboven, Christian Wolf, Felix |
| author_facet | Tarraf, Ahmad Kassem-Manthey, Koutaiba Mohammadi, Seyed Ali Martin, Philipp Moj, Lukas Burak, Semih Park, Enju Terboven, Christian Wolf, Felix |
| contents | Numerical simulations have revolutionized the industrial design process by reducing prototyping costs, design iterations, and enabling product engineers to explore the design space more efficiently. However, the growing scale of simulations demands substantial expert knowledge, computational resources, and time. A key challenge is identifying input parameters that yield optimal results, as iterative simulations are costly and can have a large environmental impact. This paper presents an AI-assisted workflow that reduces expert involvement in parameter optimization through the use of Bayesian optimization. Furthermore, we present an active learning variant of the approach, assisting the expert if desired. A deep learning model provides an initial parameter estimate, from which the optimization cycle iteratively refines the design until a termination condition (e.g.,energy budget or iteration limit) is met. We demonstrate our approach, based on a sheet metal forming process, and show how it enables us to accelerate the exploration of the design space while reducing the need for expert involvement. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_22302 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | When AI Bends Metal: AI-Assisted Optimization of Design Parameters in Sheet Metal Forming Tarraf, Ahmad Kassem-Manthey, Koutaiba Mohammadi, Seyed Ali Martin, Philipp Moj, Lukas Burak, Semih Park, Enju Terboven, Christian Wolf, Felix Artificial Intelligence Distributed, Parallel, and Cluster Computing Performance Numerical simulations have revolutionized the industrial design process by reducing prototyping costs, design iterations, and enabling product engineers to explore the design space more efficiently. However, the growing scale of simulations demands substantial expert knowledge, computational resources, and time. A key challenge is identifying input parameters that yield optimal results, as iterative simulations are costly and can have a large environmental impact. This paper presents an AI-assisted workflow that reduces expert involvement in parameter optimization through the use of Bayesian optimization. Furthermore, we present an active learning variant of the approach, assisting the expert if desired. A deep learning model provides an initial parameter estimate, from which the optimization cycle iteratively refines the design until a termination condition (e.g.,energy budget or iteration limit) is met. We demonstrate our approach, based on a sheet metal forming process, and show how it enables us to accelerate the exploration of the design space while reducing the need for expert involvement. |
| title | When AI Bends Metal: AI-Assisted Optimization of Design Parameters in Sheet Metal Forming |
| topic | Artificial Intelligence Distributed, Parallel, and Cluster Computing Performance |
| url | https://arxiv.org/abs/2511.22302 |